Llama 3.3 70B vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 3.3 70B by Meta against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
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Llama 3.3 70B
Open-Weights-ModellLlama 3.3 70B von Meta ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 128K Token zu API-Preisen von $0.0/1M Input- und $0.0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.00 / 1M
$0.00 / 1M
128K tokens
39.5%
Claude Opus 5.5
Deep-Reasoning-ModellClaude Opus 5.5 von Anthropic ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 1M Token zu API-Preisen von $4.0/1M Input- und $20.0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Wichtigste Unterschiede auf einen Blick
- Llama 3.3 70B is 164.0x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 128K tokens).
- Llama 3.3 70B is faster at 280 tok/s vs 78 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 39.5%).
| Feature | Llama 3.3 70B | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 38 | 94 |
Provider | Meta | Anthropic |
Input Price Cost per million input tokens | $0.00 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.07 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 128K |
Throughput Speed Tokens generated per second | 280 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 39.5% | 84.6% |
GPQA Diamond Graduate-level reasoning | 48.9% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.40 / 1M |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkGeprüfte Benchmark-Ergebnisse im Vergleich
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 3.3 70B · Strengths & Key Advantages
- Fortschrittliche Dense Autoregressive Transformer-Architektur von Meta mit 128K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0.0/1M Input, $0.0/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Claude Opus 5.5 · Strengths & Key Advantages
- Fortschrittliche Extended Autonomous Hybrid Reasoning Transformer-Architektur von Anthropic mit 1M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($4.0/1M Input, $20.0/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3.3 70B | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.21 | $3.85 |
Output Tokens / Task Tokens generated per task | 45K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 42K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 3.3 70B | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3.3-70B | claude-opus-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2024-12 | 2026-09 |
| Knowledge Cutoff | December 2023 | June 2026 |
| Architecture | Dense Autoregressive Transformer | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | 70B | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | $0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
Llama 3.3 70B vs Claude Opus 5.5: Schnelle Antworten (FAQ)
Which is more intelligent, Llama 3.3 70B or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 38 on the composite Intelligence Index.
Which model is faster?
Llama 3.3 70B is faster, generating 280 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
Llama 3.3 70B is cheaper at $0.07 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3.3 70B has lower latency, with a time to first token of 0.25s compared with 1.40s.
Which model has a larger context window?
Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 128K tokens.
Fazit
Choose Llama 3.3 70B for unternehmensautomatisierung, software-engineering, autonome ki-agenten und meta-produktionssysteme, or choose Claude Opus 5.5 for unternehmensautomatisierung, software-engineering, autonome ki-agenten und anthropic-produktionssysteme.
Häufig gestellte Fragen (FAQ)
Which is cheaper: Llama 3.3 70B or Claude Opus 5.5?+
Llama 3.3 70B costs $0/1M input and $0/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: Llama 3.3 70B or Claude Opus 5.5?+
Llama 3.3 70B supports 128K tokens whereas Claude Opus 5.5 supports 1M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Offizielle Quellen: Meta & Anthropic Official Documentation